Method of animating sms-messages

ABSTRACT

The present invention relates to rendering texts in a natural language, namely to manipulating a text in a natural language to generate an image or animation corresponding to this text. The technical effect achievable from the use of the claimed invention consists in expanding the range of technical facilities as related to rendering a text written in a natural language (obtaining animations using the text). The invention is unique mainly in that a sequence of animations is selected, semantically corresponding to a given text. Given a set of animations and a text, the invention makes it possible to compare the sequence of these animations to this text. It is unique in that text templates are used and a optimum sequence of these templates is determined. The idea of the templated text rendering consists in that the text is manipulated to generate an image or animation with the aid of searching correspondences to a limited number of predefined templates. An animation according to certain style is selected in compliance with each template. Animations are sequentially combined into a single sequence of video images.

TECHNICAL FIELD

The present invention relates to rendering texts in a natural language, namely to manipulating a text in a natural language to generate an image or animation corresponding to this text. More particularly, the invention relates to implementation of the “templated” text rendering process.

DESCRIPTION OF THE PRIOR ART

A variety of solutions exist enabling the text to be analyzed so that static images may be formed based on the analysis results.

Solutions exist, enabling scenes and animations to be created manually by drawing the objects, characters, scenes so that a two-dimensional (or even three-dimensional) animated clips or static images may be ultimately obtained.

However, no solutions are known which would enable an animated clip to be automatically obtained by algorithmically combining a set of preliminary made animated clips, which logically corresponds to a text in a natural language even if limited to a certain vocabulary or topics including but not limited to the use of the most commonly used words and phrases including colloquial ones.

SUMMARY OF THE INVENTION

The technical effect achievable from the use of the claimed invention consists in expanding the range of technical facilities as related to rendering a text written in a natural language (obtaining animations using the text).

According to the first aspect of the present invention, a method is provided for selecting animations corresponding to a text in a natural language, said method comprising the steps of:

receiving a text of request in a natural language;

dividing said text into sentences;

dividing said sentences into words;

reducing each word to a normalized form;

selecting a sequence of templates for the normalized text;

combining the sequences of templates for each sentence in a certain order into a single common sequence of templates;

selecting an animation for each template;

combining the selected animation files into a resulting clip.

The method of selecting animations corresponding to a text in a natural language further comprises formally replacing words, phrases and symbols with predefined words, phrases or symbols equivalent thereto.

The method of selecting animations corresponding to a text in a natural language further comprises correcting misprints in the words.

The method of selecting animations corresponding to a text in a natural language further comprises selecting if necessary neutral background animations for each template.

The method of selecting animations corresponding to a text in a natural language further comprises determining whether any sequences of templates corresponding to the normalized text are available in the cache, wherein if a sequence of templates is available in the cache, selecting the sequence of templates from the cache.

The method of selecting animations corresponding to a text in a natural language further comprises determining whether an animation style is set for the incoming text, wherein

if the style is set, selecting animations corresponding to the style provided that for the selected style, an animation of each template from the sequence is available in this style; and

if no style is set, randomly selecting the style for which all animations are available for the selected templates;

if there is a number of such styles, randomly selecting any style; if no styles are available, using a selection of animations in various styles.

The method of selecting animations corresponding to a text in a natural language further comprises saving in the database the original text of request, the selected sequence of templates; and

updating statistics for templates, words, animations, the list of unknown words and statistics for unknown words.

According to a second aspect of the present invention, a method is provided for searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates, said method comprising the steps of:

a) selecting from the list, all templates having all template words included in a sentence, wherein if a particular animation style is set, selecting templates from the list of templates having animations in the selected style rather than from the list of all templates;

b) deriving from the list of templates obtained in the previous step, the information regarding the hierarchy of templates, wherein the template hierarchy level determines the template rank;

c) if templates belonging to the same hierarchy but having different levels are included in a single set of templates obtained in step a), deleting from this set the templates with a minimum rank (a higher level);

d) selecting from the list of templates obtained in step c), an optimum set of templates for which a target function will have the most optimum value,

wherein the target function is given by:

${{f(x)} = {{{k_{1}*\frac{1}{N_{crosses}}} + {k_{2}*N_{coverage}} + {k_{3}*N_{rank}} + {k_{4}*N_{{pairs}\mspace{11mu} {in}{\; \;}{correct}\mspace{14mu} {sequence}}} - {k_{4}*N_{{pairs}\mspace{11mu} {in}\mspace{11mu} {incorrect}{\mspace{11mu} \;}{sequence}}}}->{MAX}}},$

where:

N_(crosses) is a number of crossings of the template words in the set of templates,

N_(coverage) is an aggregate coverage of all words by the templates (number of all words from a sentence, encountered in the templates),

N_(rank) is an aggregate rank of all templates from the set,

N_(pairs in correct sequence) is a number of word pairs of the composite (i.e., consisting of a few words) templates corresponding to the sequence of words in a phrase,

N_(pairs in incorrect sequence) is a number of word pairs of the composite templates not corresponding to the sequence of words in a phrase, and

k₁, k₂, k₃, k₄ are empirically calculated coefficients. They have a value of 0.4, 0.33, 0.4, 0.2, respectively.

The method of searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates further comprises searching an optimum value of the target function either by means of an exhaustive search of the sequence of templates (successively going through all combinations of unique templates) or by means of multicriteria optimization.

The method of searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates further comprises determining whether any sequences corresponding to the normalized test are available in the cache, wherein if a sequence of templates is available in the cache, selecting a sequence of templates from the cache.

The method of searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates further comprises determining whether an animation style is set for the incoming text, wherein

if the style is set, selecting animations corresponding to the style provided that for the selected style, an animation of each template from the sequence is available in this style; and

if no style is set, randomly selecting the style for which all animations are available for the selected templates;

if there is a number of such styles, randomly selecting any style; if no styles are available, using a selection of animations in various styles.

The method of searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates further comprises saving in the database the original text of request, the selected sequence of templates; and

updating statistics for templates, words, animations, the list of unknown words and statistics for unknown words.

According to a third aspect of the present invention, a second embodiment is provided of the method of searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates, said method comprising the steps of:

using the Levenshtein edit-distance algorithm, finding all templates all words of which are found in the phrase being sought;

sorting the found templates by the number of words in ascending order; sequentially determining for each template from this formed set of templates, whether the template is part of any other template from the set, wherein if the template is part of any other template from the set, deleting the same;

in the resulting set of templates, defining the templates non-crossing each other and crossing each other;

adding the non-crossing templates to the resulting set;

calculating for each template an average value of the template word positions in the text; and

sorting the templates by that average value to obtain an optimum sequence of templates.

The second embodiment of the method of searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates further comprises determining in the crossing templates the rate of crossing (number of word crosses) of the words from the set of non-crossing templates and the number of new words not included in this set, covering the phrase.

The second embodiment of the method of searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates further comprises, if no non-crossing templates exist, forming an optimum set of templates from the crossing templates, considering the following criteria: maximum aggregate rank, maximum coverage, minimum crosses; wherein

the set of templates is formed by carrying out the following steps of:

a) selecting a first template with a maximum weight, said template weight being the number of words in the template;

b) selecting templates minimally crossing the first template and non-crossing each other;

c) selecting from the remaining templates those templates that contain >=50% of new words; wherein

d) if in step a) a few templates with a maximum weight were available, selecting as the resulting set of templates the set having a maximum difference between the aggregate rank of templates and the number of word crosses.

According to a fourth aspect of the present invention, a method is provided for animation of SMS messages, said method comprising the steps of:

receiving an SMS message from a sender;

analyzing the received SMS message by defining the message text;

sending a request containing the text to an animation service;

selecting animations using the above described method of selecting animations corresponding to a text in a natural language;

combining the selected animations and sending to a service processing service;

forming an MMS containing the received animation and sending to a recipient.

The method of animation of SMS messages further comprises displaying in the SMS message the recipient's phone number and the message text.

The method of animation of SMS messages further comprises identifying in the received SMS message the sender and the recipient.

The method of animation of SMS messages further comprises sending to the sender an the SMS message specifying that the animation has been sent or failed.

According to a fifth aspect of the present invention a second embodiment is provided of the method of animation of SMS messages, said method comprising the steps of:

receiving an SMS message from a subscriber;

analyzing the received SMS message to emphasize the message text;

sending a request with text to the animation service;

selecting the image or animation using the above described method of selecting animations corresponding to a text in a natural language;

sending the selected image or animation to the service processing service;

forming an MMS containing the image or animation and sending to the subscriber.

The second embodiment of the method of animation of SMS messages further comprises identifying the subscriber in the received SMS message.

The present invention may encompass any combination of the above features or limitations except for combinations of such features which are mutually exclusive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary implementation of the animation service.

FIG. 2 shows an exemplary implementation of the SMS messages animation using the animation service.

DETAILED DESCRIPTION

The animation selection algorithm is implemented within a separate web service (FIG. 1) realized using the Microsoft ASP.NET technology. The web service refers to the database implemented under the control of the Microsoft SQL Server. The selection of technology (ASP.NET, SQL Server) and way of implementation (web service) is not obligatory. Equally well, the algorithm may be implemented in a window-based application written, for example, in a programming language (c, c#, c++, Pascal and others), and conventional text files may be selected as the database.

The web service comprises a means implementing the animation selection algorithm. As the text parameters of the animated message, a style is set wherein animation has to be selected (optionally). The algorithm generates a sequence of bytes, i.e., an assembled binary SWF file.

The algorithm (method) is unique mainly in that a sequence of animations is selected, semantically corresponding to a given text. Given a set of animations and a text, the algorithm makes it possible to compare the sequence of these animations to this text. It is unique in that text templates are used, an optimum sequence of these templates is determined, and a style concept is used.

Mode of Operation of the Web Service Performing Text Animation

The idea of the templated text rendering consists in that the text is manipulated to generate an image or animation with the aid of searching correspondences to a limited number of predefined templates. An animation according to certain style is selected in compliance with each template. Animations are sequentially combined into a single sequence of video images. The text animation algorithms are comprised in the web service developed for solving these tasks. All metadata used in the process of animation (templates, animations, styles, etc.) are store in the database (DB) with which the web service operates.

The animation being generated is templated. Therefore, it is a main object of the service to split the text to be animated into semantically close templates according to a certain sequence, to select the respective animation for each template and to combine the animations into a single clip. The animation may be styled someway (smiley, Disney, Winnie-the-Pooh, Bidstrup, anti-animation, etc.). For testing and deployment of the animation service, it is “wrapped” into a web interface whose main object is to generate animations, to administer templates, animations, to accumulate statistics, etc.

In order to determine the structure of the web service, a number of definitions will be given.

Style

A visual style is to be satisfied at the output. For example, smiley, cartoons, serious. At least one style exists: “on default”.

Templates

Templates are a selection of phrases in the form of “I #to love you”, “Hi”, “To kiss you”. Each template is stored as a selection of normalized words. It means that “To kiss you” is stored instead of “I kiss you”. The template is normalized in the step of adding via the web interface. Registers are ignored in the templates. The number of interword spaces and word separation by punctuation is ignored.

Group

Groups are preceded by a tag #. A group is an aggregation of a number of symbols. For example, #love—love, adore, in love, wild about. Thus the group #love aggregates the words “adore”, “love” and others. The groups make it possible to define faster similar templates. A template not comprising a group has a higher priority in terms of selecting a template.

Animation

A number of animations correspond to each template. Animation has to be specified with reference to the style. A number of animations may exist in the same style. Then, one of them is selected. For each template established in the system, at least one animation in the “on default” style exists unless other style is explicitly established. Template animation—a content file in the swf format (the format may be both vector and bit mapped). The animation has a “background animation” feature defining that the animation is background. Non-background animations comprise a transparent background. Some background animations suitable for demonstrating arbitrary foreground animations are neutral background.

Misprints

Misprints may occur in the text to be transmitted. Misprints are corrected by means of the misprinted word=>correct word relationship set in store.

User

A user is the one who makes an animation request by sending a text. It may be a service, web application, etc. The user is entitled to use the service within certain time limits. The user has the authentication data stored in the database. The database also stores the information regarding the time for authorized access to the service. A set of allowed animation styles may be associated with the user.

In addition to processing of the text animation requests, the web service accumulates statistics (FIG. 1) of the use of templates (frequency), occurrence of words in the text, occurrence of words not found in any template, frequency of use of styles, frequency of use of animations.

The web service comprises means implementing the text animation selection algorithm and means enabling this animation to be returned to the user and to be sent to the recipient

Animation Selection Algorithm Corresponding to a Text

At the input of the algorithm, are the text and style wherein the respective animations have to be selected (optionally). This data is sent to web service.

1. The input text is split into sentences. Splitting is performed with the aid of standard punctuation separators (“full stop”, “interrogation mark”, “exclamation mark”, “ellipsis”). Splitting is performed by means of a normal search of separators.

2. The sentence is split into parts based on word separators (splitting is performed by means of a normal search). Separators include (“full stop”, “comma”, “space”, “colon”, “dash”, “semicolon”. Space, carriage return, tabulation characters are cut off from the resulting words at their beginning and end.

3. If all words in the text are English words, transliteration is performed. This is done by means of a symbol-by-symbol comparison of the sequences of English letters to phonetically close sequences of Russian letters (simple substitution table); if the algorithm is used for an English-language or any other content, transliteration may be omitted.

4. Before subsequent normalization, textual substitutions are made according to the substitution dictionary. It allows the words, word combinations and sentences as well as special characters to be substituted with other characters corresponding to more efficient rendering.

5. Each word is reduced to a normalized form (stemmer). The selection of stemmer is of no special importance. ALP-enabled (automated language processing, http://www.aot.ru/) Lucene.NET (http://www.dotlucene.net/) stemmer may be used as an example for normalizing a Russian-language text;

6. The words are checked for standard misprints to replace the misprinted words with the correct ones. A general dictionary of the words (normal forms) included in all sets of templates is maintained and stored in the DB. There are two methods of search and replace:

a simple replacement method according to a dictionary of misprints, containing correct word meanings from a general dictionary of standard misprints;

a method of searching a correct word with a minimum Levenshtein distance to the incorrect word being sought for subsequent replacement. Correction of misprints is an optional algorithm step. Correction of misprints may be also performed before the words are normalized.

7. The words being part of the groups (generalizations of synonyms) are replaced with the group names. As a group name, a synonym is usually selected however the group name may be in general arbitrary.

8. It is checked whether the cache contains any sequences of templates corresponding to the normalized text free of misprints, obtained in step 6. If so, a sequence of templates is selected from the cache and the process goes to step 11. The cache is selection of pairs (corrected normalized text; sequence of templates) stored in the random-access memory (for fast fetching) and not in the database;

9. If no text to be animated is available in the cache, a sequence of templates is selected for the normalized text using the algorithm for selection of templates corresponding to the animation; the sequence of templates is defined by the algorithm for selection of templates corresponding to the animation.

10. The resulting sequence of templates is added to the cache by associating it with the text being sought.

11. Steps 2-10 are repeated for each sentence.

12. The sequences of templates for each sentence are combined in a certain order into a single common sequence of templates.

13. If a style is set, animations are selected corresponding to the style provide that for the selected style, an animation of each template from the sequence is available in this style. If no style is set, a style is randomly selected for which all animations are available for the selected templates. If there is a number of such styles, any style is randomly selected. If no styles are available, a selection of animations in various styles is used.

14. An animation is selected for each template in the selected style. If a single template has more than one corresponding animations in one style, an random animation is selected. Animations may be divided (optionally) into foreground and background animations. There are a number of options for “splicing” the animated clips selected into a single one based on the sequence of templates:

sequential “splicing” of animated files (clips) into a single one

“splicing” of foreground animations at the first or last “splicing” of the background animation in the sequence order

selecting a random neutral background for displaying foreground animations (background animations are displayed as they are).

All animated clips are scaled according to a maximum duration of clip from the sequence by centering the same (scaling and positioning algorithms may vary). In addition to the resulting clip, sound may also be superimposed in the form of an arbitrary audio composition.

15. The “spliced” video clip is converted into an arbitrary format, either vector or bit mapped format. For the initial swf animations, it may be, for example, a swf or MPEG file. The output format of the resulting animated file is of no importance.

16. The original text of request, the selected sequence of templates are saved in the database; statistics for templates, words, animations, the list of unknown words and statistics for unknown words is updated.

Algorithm for selection of templates corresponding to the animation

The algorithm is used to select animations corresponding to a text.

Algorithm input data: list of templates (is cached upon starting the web service), normalized text of the sentence without misprints. The list of templates is stored in the database in the normalized form.

1. In order to determine the sequence of templates, all templates having all template words included in a sentence are first selected from the list. If a particular animation style is set, the templates are selected from the list of templates having animations in the selected style rather than from the list of all templates.

2. Information regarding the hierarchy of templates is derived from the list of templates. Template A is said to be higher than template B in the hierarchy, if all template A words are included in template B. The hierarchy level is determined by the template rank.

3. If the templates belonging to the same hierarchy but having different levels are included in a single set of templates obtained in step 1, the templates with a minimum rank (a higher level) are deleted from this set.

4. From the list of templates obtained in step 2, an optimum set of templates is selected, for which a target function will have the most optimum value. The target function is given by:

${{f(x)} = {{{k_{1}*\frac{1}{N_{crosses}}} + {k_{2}*N_{coverage}} + {k_{3}*N_{rank}} + {k_{4}*N_{{pairs}\mspace{11mu} {in}{\; \;}{correct}\mspace{14mu} {sequence}}} - {k_{4}*N_{{pairs}\mspace{11mu} {in}\mspace{11mu} {incorrect}{\mspace{11mu} \;}{sequence}}}}->{MAX}}},$

where:

N_(crosses) is a number of crossings of the template words in the set of templates,

N_(coverage) is an aggregate coverage of all words by the templates (number of all words from a sentence, encountered in the templates),

N_(rank) is an aggregate rank of all templates from the set,

N_(pairs in correct sequence) is a number of word pairs of the composite (i.e., consisting of a few words) templates corresponding to the sequence of words in a phrase,

N_(pairs in incorrect sequence) is a number of word pairs of the composite templates not corresponding to the sequence of words in a phrase, and

k₁, k₂, k₃, k₄ are empirically calculated coefficients. They have a value of 0.4, 0.33, 0.4, 0.2, respectively.

The value of function depends on a number of mathematical criteria. The function optimum (its maximum value) may be found in a number of ways:

1. by numerical techniques of multicriteria optimization;

2. by means of an exhaustive search of all sets of templates to calculate the target value function for each template

Simplified algorithm for determining the sequence of templates corresponding to the animation

A simplified and faster version of the algorithm for determining the sequence of templates corresponding to the animation is as follows:

1. Using the BlockDistance/Levenshtein edit-distance/Jaro-Winkler distance/Damerau-Levenshtein distance algorithm, all templates are identified wherein all words are in the phrase being sought.

2. The identified templates are sorted in ascending order by the number of words. Sequentially running through this form set of templates, it is checked whether a template is part of another template from the set. If so, it is discarded.

Thus, a list of templates without enclosures is obtained.

3. In the resulting set of templates, the templates non-crossing each other and crossing each other are defined.

4. (Optional) In the crossing templates, the rate of crossing (number of word crosses) is determined of the words from the set of non-crossing templates and the number of new words not included in this set, covering the phrase.

5. Non-crossing templates are added to the resulting set (these are known to be included).

6. (Optional) If no non-crossing templates exist, the process goes to step 7. From the remaining crossing templates, a set is attempted to be formed, being optimum by the following criteria: maximum aggregate rank, maximum coverage, minimum crosses. This is done as follows:

6.1 a first template is used with a maximum weight (weight=number of words);

6.2 templates are then used minimally crossing the first template and not crossing each other;

6.3 from the remaining templates, those templates are further selected which contain >=50% of new words: templates are sorted by the number of new words and are added one at a time. In doing so, a test for word novelty is carried out considering the templates being added in the process (i.e., of the first template was added with a number of new words being over 50%, the second template may not be added since considering the words of the first added template the number of new words therein will be less).

6.4 If a few templates with a maximum rank were used in step 6.1, for the set obtained in step 6.3, a value is calculated as follows: aggregate rank templates minus number or word crosses. Then, the next template with a maximum rank is used and steps 6.2-6.4 are repeated.

6.5. A set of templates is selected with a maximum value calculated in step 6.4.

7. The resulting optimum set of templates has to be transformed into a sequence. To this end, an average value of the template word positions in the text is calculated for each template, and templates are sorted by this average value. The obtained sequence comprises the desired result.

The simplified version of the algorithm may be complicated by adding further search criteria for an optimum sequence of templates from the complete version of the algorithm.

Algorithm for determining the sequence of templates corresponding to the animation

In order to determine the sequence of templates, a simple algorithm is used whose input data is a text of sentence and a set of templates able to form a most full coverage of the sentence.

1. For each template, its mean position in the sentence is calculated: an arithmetic mean of the sequential word numbers in the text. If a template covers some identical words, the first one is used (an embodiment is possible wherein an arithmetic mean of all positions is used).

2. Templates are arranged in ascending order by the mean position in the sentence.

Method of Animation of SMS Messages (FIG. 2)

Two options are available for animation of SMS messages.

1. Draw a Message to Your Friends!

A person sends a SMS message to a short number, specifying a subscriber to which the message has to be forwarded. The most appropriate animation closest to the message subject is identified and sent to said subscriber. Both a static image and animation may be sent.

2. Visualize Your Wishes!

A person sends a SMS message containing the desired object to a short number and receives a funny picture of this object.

Below is the operating sequence of an embodiment wherein animated messages are used for sending to friends.

1. A sending subscriber sends a SMS message to a short number. In the SMS message, he includes a recipient's subscriber number and the message text.

2. The message is routed through the mobile network operator's (MNO) equipment to the SMS Center (SMSC) from where the data is provided to the billing system and the message itself is forwarded to the service processing service (a moderator is possibly used).

3. The service processing service analyzes the received SMS message by identifying the sender and defining the message text and the recipient's number.

4. The service processing service makes a request containing the text to animation service.

5. The animation service generates (selects) animation by selecting the respective data from a certain animation database.

6. The selected animation templates are assembled and sent to the service processing service.

7. The service processing service forms an MMS containing the received animation and sends it via the MMS Center (MMSC) to the recipient subscriber.

8. The service processing service sends an SMS message to the sending subscriber via the SMSC saying that the animation has been sent or failed if no MMS has been sent (optionally).

9. The recipient subscriber receives the MMS containing the animated SMS message on behalf (from the number) of the recipient.

10. The sending subscriber receives an SMS message notifying of sending (optionally).

The operating sequence of an embodiment wherein wishes are visualized is somewhat different although the flow chart remains very similar for the participants in the process with the only exception that the recipient subscriber coincides with the sending subscriber.

1. The sending subscriber sends a SMS message to a short number. He specifies the desired object in the SMS message.

2. The message is routed through the mobile network operator's (MNO) equipment to the SMS Center (SMSC) from where the data is provided to the billing system and the message itself is forwarded to the service processing service.

3. The service processing service analyzed the received SMS message by identifying the sender, defining the object to be visualized and forwarding its textual description to the animation service.

4. The animation service selects a picture/animation by selecting the respective data from the animation database and sends the same to the service processing service.

5. The selected animation is formed into an MMS and sent via the MMS Center (MMSC) to the subscriber.

6. The subscriber receives the MMS containing the animated SMS message.

The accompanying drawings illustrate the architecture, functionality and operation of the possible implementations of the systems and methods according to various embodiments of the present invention. Accordingly, each block may comprise a module, segment or portion of code which contains one or more executable commands for implementing a certain logical function(s). It should be also noted that in some alternative embodiments, the functions designated in the block may be performed in an order other than that shown in the drawings. For example, two blocks shown in series may actually be executed in a reverse order depending in the enabled functionality. It should be also noted that each illustrated block and combinations of blocks in the drawings may be implemented either as specialized software-based systems and modules which perform specialized functions or actions or as combinations of the specialized software and computer commands

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention to the particularly provided example. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising”, “includes” and/or “including” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.

While particular embodiments of the invention have been illustrated and decribed above, it will be apparent to those of ordinary skill in the art that any configuration designed for achieving the same object may be presented instead of the particularly shown embodiments and that the invention has other applications in other environments. The present disclosure is intended to encompass any adaptations or variations of the present invention. The following claims are by no means intended for limiting the scope of invention to particular embodiments described herein. 

1. A method for selecting animations corresponding to a text in a natural language, said method comprising the steps of: receiving a text of request in a natural language; dividing said text into sentences; dividing said sentences into words; reducing each word to a normalized form; selecting a sequence of templates for the normalized text; combining the sequences of templates for each sentence in a certain order into a single common sequence of templates; selecting an animation for each template; combining the selected animation files into a resulting clip.
 2. The method of claim 1, characterized in that words, phrases and symbols are formally replaced in the predefined words, phrases or symbols equivalent thereto.
 3. The method of claim 1, characterized in that misprints are corrected in the words.
 4. The method of claim 1, characterized in that neutral background animations for each template are selected if necessary.
 5. The method of claim 1, characterized in that it is determined whether any sequences of templates corresponding to the normalized text are available in the cache, wherein if a sequence of templates is available in the cache, the sequence of templates is selected from the cache.
 6. The method of claim 1, characterized in that it is determined whether an animation style is set for the incoming text, wherein if the style is set, animations corresponding to the style are selected provided that for the selected style, an animation of each template from the sequence is available in this style; and if no style is set, the style is randomly selected for which all animations are available for the selected templates; if there is a number of such styles, any style is randomly selected; if no styles are available, a selection of animations in various styles is used.
 7. The method of claim 1, characterized in that the original text of request, the selected sequence of templates are saved in the database; and statistics for templates, words, animations, the list of unknown words and statistics for unknown words is updated.
 8. A method for searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates, comprising the steps of: a) selecting from the list, all templates having all template words included in a sentence, wherein if a particular animation style is set, selecting templates from the list of templates having animations in the selected style rather than from the list of all templates; b) deriving from the list of templates obtained in the previous step, the information regarding the hierarchy of templates, wherein the template hierarchy level determines the template rank; c) if templates belonging to the same hierarchy but having different levels are included in a single set of templates obtained in step a), deleting from this set the templates with a minimum rank (a higher level); d) selecting from the list of templates obtained in step c), an optimum set of templates for which a target function will have the most optimum value, wherein the target function is given by: ${{f(x)} = {{{k_{1}*\frac{1}{N_{crosses}}} + {k_{2}*N_{coverage}} + {k_{3}*N_{rank}} + {k_{4}*N_{{pairs}\mspace{11mu} {in}{\; \;}{correct}\mspace{14mu} {sequence}}} - {k_{4}*N_{{pairs}\mspace{11mu} {in}\mspace{11mu} {incorrect}{\mspace{11mu} \;}{sequence}}}}->{MAX}}},$ where: N_(crosses) is a number of crossings of the template words in the set of templates, N_(coverage) is an aggregate coverage of all words by the templates (number of all words from a sentence, encountered in the templates), N_(rank) is an aggregate rank of all templates from the set, N_(pairs in correct sequence) is a number of word pairs of the composite (i.e., consisting of a few words) templates corresponding to the sequence of words in a phrase, N_(pairs in incorrect sequence) is a number of word pairs of the composite templates not corresponding to the sequence of words in a phrase, and k₁, k₂, k₃, k₄ are empirically calculated coefficients. They have a value of 0.4, 0.33, 0.4, 0.2, respectively.
 9. The method of claim 8, characterized in that an optimum value of the target function is searched either by means of an exhaustive search of the sequence of templates (successively going through all combinations of unique templates) or by means of multicriteria optimization.
 10. The method of claim 8, characterized in that it is determined whether any sequences corresponding to the normalized test are available in the cache, wherein, wherein if a sequence of templates is available in the cache, the sequence of templates is selected from the cache.
 11. The method of claim 8, characterized in that it is determined whether an animation style is set for the incoming text, wherein if the style is set, animations corresponding to the style are selected provided that for the selected style, an animation of each template from the sequence is available in this style; and if no style is set, the style is randomly selected for which all animations are available for the selected templates; if there is a number of such styles, any style is randomly selected; if no styles are available, a selection of animations in various styles is used.
 12. The method of claim 8, characterized in that the original text of request, the selected sequence of templates are saved in the database; and statistics for templates, words, animations, the list of unknown words and statistics for unknown words is updated.
 13. The method of searching an optimum sequence of templates for subsequently selecting animations corresponding to the sequence of templates, said method comprising the steps of: using the Levenshtein edit-distance algorithm, finding all templates all words of which are found in the phrase being sought; sorting the found templates by the number of words in ascending order; sequentially determining for each template from this formed set of templates, whether the template is part of any other template from the set, wherein if the template is part of any other template from the set, deleting the same; in the resulting set of templates, defining the templates non-crossing each other and crossing each other; adding the non-crossing templates to the resulting set; calculating for each template an average value of the template word positions in the text; and sorting the templates by that average value to obtain an optimum sequence of templates.
 14. The method of claim 13, characterized in that the rate of crossing (number of word crosses) of the words from the set of non-crossing templates and the number of new words not included in this set, covering the phrase is determined in the crossing templates.
 15. The method of claim 13, characterized in that if no non-crossing templates exist, an optimum set of templates is formed from the crossing templates, considering the following criteria: maximum aggregate rank, maximum coverage, minimum crosses; wherein the set of templates is formed by carrying out the following steps of: a) selecting a first template with a maximum weight, said template weight being the number of words in the template; b) selecting templates minimally crossing the first template and non-crossing each other; c) selecting from the remaining templates those templates that contain >=50% of new words; wherein d) if in step a) a few templates with a maximum weight were available, selecting as the resulting set of templates the set having a maximum difference between the aggregate rank of templates and the number of word crosses.
 16. A method of animation of SMS messages, said method comprising the steps of: receiving a SMS message from a sender; analyzing the received SMS message by defining the message text; sending a request containing the text to an animation service; selecting animations using the method of claim 1; combining the selected animations and sending to a service processing service; forming an MMS containing the received animation and sending to a recipient.
 17. The method of claim 16, characterized in that the recipient's subscriber number and the message text are included in the SMS message.
 18. The method of claim 16, characterized in that the sender and the recipient are identified in the received SMS message.
 19. The method of claim 16, characterized in that a SMS message is sent to the sender saying that the animation has been sent or failed.
 20. A method of animation of SMS messages, said method comprising the steps of: receiving a SMS message from a subscriber; analyzing the received SMS message by defining the message text; sending a request containing the text to an animation service; selecting and image or animations using the method of claim 1; sending the selected image or animations to a service processing service; forming an MMS containing the received image or animation and sending to a subscriber.
 21. The method of claim 20, characterized in that the subscriber is identified in the received SMS message. 